Artificial intelligence (AI) has transformed eye disease screening. Deep learning systems can now automatically detect diseases from retinal images. This chapter examines current AI technologies, including convolutional neural networks, vision transformers, and foundation models. These systems diagnose diabetic retinopathy, glaucoma, age-related macular degeneration, and anterior segment diseases. Clinical studies show a sensitivity of 87–98% and a specificity of 87–98% across large patient datasets. Implementation requires understanding Food and Drug Administration (FDA) and European Union (EU) regulations, healthcare system integration through Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) standards, and addressing algorithmic fairness to ensure equal performance across all patient groups. New technologies like federated learning allow hospitals to collaborate on AI training while keeping patient data private. Edge computing enables screening in remote areas without internet access. Vision-language models provide clear explanations of diagnoses in natural language. Current challenges include a 5–15% accuracy loss when systems are moved between hospitals and the need for uncertainty quantification. Future directions focus on combining multiple data sources, including retinal images and electronic health records.
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